How many pitches result in a mandate?
What is your pitch:win ratio? How many of your pitches become mandates? Or, put another way, how often is participating in a pitch a waste of your time?
It could be an uncomfortable question. After considering it, my gut answer is that probably only 5-20% of pitches result in an immediate business win 1. And the reason for such a wide range is because there are a number of factors at play:
Most of these are relatively fixed, or alternatively out of control of the pitching banker. But there is one element that is not frequently used enough in investment banking: customer data.
Another question for you, how many accelerated share repurchase pitches did your firm make last year? Not sure?
“If you can’t measure it, you can’t manage it.” This quote commonly misattributed to Edward Dennings (it’s actually closer to a Peter Drucker quote and frankly, heavily over-shared on LinkedIn), is, in this instance, applicable. Knowing the number of pitches means you can better manage the process of pitching.
So why is this unknown?
The reason for this obfuscation is that as a rule, the data isn’t accurately recorded. While most banks use some form of CRM, many bankers avoid manual data entry as much as possible.
Management teams have wide-ranging debates as to why this is. Are bankers lazy? (I think anyone who signs up for a career requiring 80+ hours a week is probably not lazy). Do bankers want to avoid providing management with transparency into what they are doing? (admittedly, this could occasionally be the case, but wouldn’t explain the blanket avoidance of data entry). Instead, I offer an opposing view. Bankers hate data entry because it is not rewarding.
By rewarding, I don’t mean monetary compensation, but intelligence. Insightful information that can be used. If by entering data about an accelerated pitch into a CRM and that CRM could predict which of your clients would be most likely to execute such a trade, wouldn’t there be more incentive to add that information? Or what about adding details about another type of product learned from the pitch? That’s a better incentive than just adding data for storage purposes only, right?
In an ideal world, an investment banker could use predictive analytics to determine which products to pitch to which client at what point in time. And timeliness is really the critical factor here, as every client action has a window of “ideal time” and bankers have to move quickly to execute within.
There are three significant steps to making this a reality:
1. Which clients are the most receptive?
Identifying which clients are likely to be the most receptive to a particular product pitch is an interesting problem that I, and Pellucid’s other co-founders, have spent a lot of time working on over the course of our careers.
In short, it can be done but requires a lot of work (think months not hours). You need to hypothesize the triggers and build the models, then assemble these for every single product you sell. No small task. But can give a huge advantage if you get it right.
Back when we worked in investment banking and tackled this question, we created a series of models for the esoteric products that banks sell, which triggered a combination of market, financial statement, capital structure instruments, and sentiment data.
The only part of this that required input from the bankers was the sentiment data piece—a critical element of the model. For instance, if a CFO says she never wants to see a convertible note pitch ever again, then this has to be noted as it has an impact on the probability of receiving a mandate for a contingent FX hedge.
This information, which would only be known by a “trusted advisor” is a key ingredient, as the attitude of the CFO or Treasurer is a critical predictor for the reception of alternative product pitches. Knowing that the CFO in the earlier example doesn’t want anything to do with any form of financial engineering—knowledge that could only be passed on by the convertible bond marketer—allows future pitch teams to adjust their strategies.
2. Timing is critical
In addition to sentiment data, timing triggers are critical, too. Many triggers are market driven as the market itself is the primary driver of pricing for most financial instruments. For instance, the ideal timing of unwinding an interest rate swap has a lot to do with movements in the interest rate curve. So any predictive model needs to account for the known (preferences, company strategy etc.) and unknown market movements.
3. Speed to meeting
Regardless if the trigger is company or market-based, the probability of a pitch getting the mandate or an execution green light has a lot to do with pitch speed, which suffers from the limiting factor of how quickly can you pull together a pitchbook.
Imagine if one day your predictive model alerts you to the fact that today would be a great time to get pitches out for two products to four clients. Eight pitchbooks to put together in less than a day. That’s a tall order.
To react quickly to these opportunities, you need to use a knowledge creation platform, such as Pellucid. It reduces the time required to build content from days to minutes. Meaning creating eight pitchbooks in a day is not only achievable but if you leverage pitchbook and content templates, is something you can get done before lunch.
What are your thoughts on how to fix your pitch:win ratio? Email me your thoughts at email@example.com.
The value of building relationships, learning about clients, and getting a foot in the door should not be discounted and could lead to eventual business and more invitations to pitch. ↩